{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.00000000e+00  8.33559388e+00  3.89838400e-03]\n",
      " [ 8.33559388e+00  1.66628396e+01  3.89209631e-03]\n",
      " [ 1.66628396e+01  2.49817373e+01  3.88580408e-03]\n",
      " ...\n",
      " [ 4.16567652e+03  4.16569739e+03 -3.27000604e-03]\n",
      " [ 4.16569739e+03  4.16570991e+03 -3.27654662e-03]\n",
      " [ 4.16570991e+03  4.16571408e+03 -3.28248943e-03]]\n"
     ]
    }
   ],
   "source": [
    "import pprint\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from math import *\n",
    "import pandas as pd\n",
    "n = 1000\n",
    "para = np.linspace(1.08, 0, n)\n",
    "r = 50\n",
    "# 渐开线参数方程:\n",
    "x = r * (np.cos(para + 1.082) + para * np.sin(para + 1.082))\n",
    "y = r * (np.sin(para + 1.082) - para * np.cos(para + 1.082))\n",
    "# plt.plot(x-x[-1],y-y[-1])\n",
    "# plt.show()\n",
    "\n",
    "v = 0.007\n",
    "# 离散化:\n",
    "k = 0\n",
    "vx = np.zeros(n - 1)\n",
    "vy = np.zeros(n - 1)\n",
    "time = np.zeros(n)\n",
    "# 输出矩阵 fvx和fvy是水平和竖直方向速度的分段函数\n",
    "fvx = np.zeros((n - 1, 3))\n",
    "fvy = np.zeros((n - 1, 3))\n",
    "while k <= n - 2:\n",
    "    s = sqrt((x[k] - x[k + 1]) ** 2 + (y[k] - y[k + 1]) ** 2)\n",
    "    dt = s / v\n",
    "    time[k + 1] = time[k] + dt\n",
    "    dx = x[k+1] - x[k]\n",
    "    dy = y[k+1] - y[k]\n",
    "    vx[k] = dx / dt\n",
    "    vy[k] = dy / dt\n",
    "    fvx[k, 0] = time[k]\n",
    "    fvx[k, 1] = time[k + 1]\n",
    "    fvx[k, 2] = vx[k]\n",
    "    fvy[k, 0] = time[k]\n",
    "    fvy[k, 1] = time[k + 1]\n",
    "    fvy[k, 2] = vy[k]\n",
    "    k += 1\n",
    "    pass\n",
    "print(fvx)\n",
    "fvx = pd.DataFrame(fvx)\n",
    "fvy = pd.DataFrame(fvy)\n",
    "fvx.to_excel('fvx.xlsx', index=False, header=None)\n",
    "fvy.to_excel('fvy.xlsx', index=False, header=None)\n",
    "fvx = np.array(fvx)\n",
    "fvy = np.array(fvy)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "data": {
      "text/plain": "(999, 3)"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fvx.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "from 画图 import *\n",
    "k=0\n",
    "A=np.mat([0,0])\n",
    "while k<=fvx.shape[0]-2:\n",
    "    dt=fvx[k,1]-fvx[k][0]\n",
    "    dx=fvx[k,2]*dt\n",
    "    dy=fvy[k,2]*dt\n",
    "    B=A[k]+np.mat([dx,dy])\n",
    "    A=np.append(A,B,axis=0)\n",
    "    k+=1\n",
    "    pass\n",
    "A=A.tolist()\n",
    "A=np.array(A)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ -3.          -0.08034215]\n",
      " [ -2.99566268  -0.08034215]\n",
      " [ -2.99132537  -0.08034216]\n",
      " ...\n",
      " [  7.67980201 -21.63758237]\n",
      " [  7.6798023  -21.62834296]\n",
      " [  7.67980259 -21.63296265]]\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 6000x4000 with 1 Axes>",
      "image/png": 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     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from 读取文件 import *\n",
    "from Curve_fitting import *\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "def Jkx_std(Y, n=6):  # 理论渐开线中, x与y的关系\n",
    "    t = np.linspace(0, 1.06, 1000)\n",
    "    r = 50\n",
    "    w = 1\n",
    "    x = r * (np.cos(w * t + 1.082) + w * t * np.sin(w * t + 1.082))\n",
    "    x = x - x[0]\n",
    "    y = r * (np.sin(w * t + 1.082) - w * t * np.cos(w * t + 1.082))\n",
    "    y = y - y[0]\n",
    "    re1 = CF(y, x, n=n)[\"Factor\"]\n",
    "    return np.polyval(re1, Y)\n",
    "\n",
    "\n",
    "filename = \"正向仿真数据/手动设置时间步/10V0.006.txt\"\n",
    "filename = np.array(loadDatadet(filename))\n",
    "x_0 = 0\n",
    "y_0 = 0\n",
    "\n",
    "print(filename)\n",
    "x = filename[:, 0] + x_0\n",
    "y = filename[:, 1] + y_0  # 平移到以原点为中心\n",
    "\n",
    "ax = plt.gca()\n",
    "plt.scatter(x, y, s=0.1)\n",
    "\n",
    "ax.grid()\n",
    "\n",
    "plt.xlim(-15, 15)\n",
    "plt.ylim(-30, 0)\n",
    "ax.grid()\n",
    "plt.scatter(A[:,0],A[:,1],0.1)\n",
    "\n",
    "plt.rcParams['savefig.dpi'] = 2000  # 图片像素\n",
    "plt.rcParams['figure.dpi'] = 2000\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}